preprints.org > computer science and mathematics > artificial intelligence and machine learning > doi: 10.20944/preprints202205.0302.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 21 May 2022 / Approved: 23 May 2022 / Online: 23 May 2022 (10:55:45 CEST)

Melanoma is considered to be the most aggressive form of skin cancer. Due to the similar shape of malignant and benign cancerous lesions, doctors spend considerably more time when diagnosing these findings. At present, the evaluation of malignancy is performed primarily by invasive histological examination of the suspicious lesion. Developing an accurate classifier for early and efficient detection can minimize and monitor the harmful effects of skin cancer and increase patient survival rates. This paper proposes a multi-class classification task using the CoAtNet architecture, a hybrid model that combines the depthwise convolution matrix operation of traditional convolutional neural networks with the strengths of Transformer models and self-attention mechanics to achieve better generalization and capacity. The proposed multi-class classifier achieves an overall precision of 0.901, recall 0.895, and AP 0.923, indicating high performance compared to other state-of-the-art networks.

skin cancer; melanoma; computer-aided diagnostics; image classification; CoAtNet; convolutional neural networks; deep learning

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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